It is known in many applications, including self-propelled vehicle applications as seen by reference to U.S. Pat. No. 6,394,208 entitled “STARTER/ALTERNATOR CONTROL STRATEGY TO ENHANCE DRIVEABILITY OF A LOW STORAGE REQUIREMENT HYBRID ELECTRIC VEHICLE” issued to Hampo et al., to employ a dynamoelectric machine in a first mode as a motor in order to provide propulsion torque. In such applications, it is also known to reconfigure the dynamoelectric machine in a second mode as a generator, in order to capture and convert some of the potential or kinetic energy associated with the application into output electrical power, a process known as regeneration (“regenerative energy”). Moreover, in such applications, it is also known to provide an energy system, such as a battery, to power the dynamoelectric machine when operated as a motor, and to receive the regenerative energy when the dynamoelectric machine is operated as a generator. In the latter case, the regenerative energy is generally operative to increase the state of charge of the battery, until such battery is “fully” charged. Battery technologies typically used in such applications include nickel metal hydride (NiMH), lead acid (PbA) and nickel cadmium (NiCd) technologies, although energy systems employing lithium chemistry technologies, while not as prevalent as other battery technologies, are also used in practice.
One aspect of the above systems that involves tradeoffs or compromises pertains to optimizing the utilization of the energy system through charging regimens. Energy systems can be presented with at least two different types of load profiles by associated applications. One such type is “energy-based,” which means that the load profile produced by the application is substantially constant. An example of this type of load profile would be an energy system associated with an automobile that is operated fairly constantly on an open highway where the operator is not accelerating or decelerating rapidly. This type of operation does not utilize a lot of power, rather it uses more energy. Another type of load profile is a “power-based” load profile, which means that the application with which the energy system is associated is presenting a more dynamic load profile to the energy system. An example of this type of load profile would be an energy system associated in an automobile where the operator is accelerating and decelerating quickly, requiring more power to be used. Presently, most optimization is done in the design phase of the energy system, as opposed to “real time” optimization done while the energy system is “live” in operation, by implementing fixed routines where “expected” customer load profiles are developed, and the energy system is designed around these expected cycles. With respect to energy systems comprised of lithium chemistry technologies, fixed energy-based balancing methods are utilized in an effort to maximize performance for energy-based applications and load profiles, however, no provisions are made to account for more power-based applications.
These existing methods, while adequate, do not allow for the most useful method of optimization. Existing methods, as set forth above, neither provide for “real time” adaptation of the charging regimen to the energy system while it is “live” in operation, nor take into account the varied applications that may be presented to an energy system. For example, while it would generally be desirable to charge the battery to its highest possible state of charge for more constant, energy-based applications presented to the energy system (which in turn would provide the greatest range or longest duration use for the application running off the battery), such an approach is generally not considered optimal for more dynamic, power-based applications. For smooth or constant, energy-based applications, the system operator will want the energy system to be charged to the highest possible state of charge in order to allow for the longest duration of use. However, for dynamic, power-based applications, where the application operator wants big surges of power as opposed to large amounts of energy for constant, smooth use, he may want to charge the energy system to a lower percentage state of charge (i.e., 50% state of charge), thereby optimizing the systems power level to allow for more power in and out of the system.
There is, therefore, a need for a process that allows the energy system to self-learn the type of application that it is being presented, and to then determine a charging strategy that will minimize or eliminate one or more of the above-identified problems.